Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems

ABSTRACT

Systems and methods of operating the same are introduced for populating and using lookup tables with process facility control systems and models of the same. An exemplary computer system for use with a process facility having a plurality of associated processes, and includes both a memory and a processor. The memory is capable of maintaining (i) a data structure having a plurality of accessible fields and (ii) a model of at least a portion of the plurality of associated processes. The model may include a mathematical representation of at least a portion of the at least one process, defining certain relationships among inputs and outputs of the at least one process. The processor is capable of populating ones of the plurality of accessible fields of the data structure using the model iteratively with a range of possible values of the at least one measurable characteristic. The computer system is capable of using the range of possible values of the at least one measurable characteristic to predict an unforced response associated with the at least one process.

CROSS-REFERENCE TO RELATED PATENT DOCUMENTS

[0001] The present invention is related to that disclosed in (i) U.S.Pat. No. 5,351,184 entitled “METHOD OF MULTIVARIABLE PREDICTIVE CONTROLUTILIZING RANGE CONTROL;” (ii) U.S. Pat. No. 5,561,599 entitled “METHODOF INCORPORATING INDEPENDENT FEEDFORWARD CONTROL IN A MULTIVARIABLEPREDICTIVE CONTROLLER;” (iii) U.S. Pat. No. 5,574,638 entitled “METHODOF OPTIMAL SCALING OF VARIABLES IN A MULTIVARIABLE PREDICTIVE CONTROLLERUTILIZING RANGE CONTROL;” (iv) U.S. Pat. No. 5,572,420 entitled “METHODOF OPTIMAL CONTROLLER DESIGN OF MULTIVARIABLE PREDICTIVE CONTROLUTILIZING RANGE CONTROL” (the “'420 Patent”); (v) U.S. Pat. No.5,758,047 entitled “METHOD OF PROCESS CONTROLLER OPTIMIZATION IN AMULTIVARIABLE PREDICTIVE CONTROLLER;” (vi) U.S. patent application Ser.No. 08/490,499, filed on Jun. 14, 1995, entitled “Method of ProcessController Optimization in a Multivariable Predictive Controller;”(vii)U.S. patent application Ser. No. 08\850,288 entitled “SYSTEMS ANDMETHODS FOR GLOBALLY OPTIMIZING A PROCESS FACILITY;”(viii) U.S. patentapplication Ser. No. 08\851,590 entitled “SYSTEMS AND METHODS USINGBRIDGE MODELS TO GLOBALLY OPTIMIZE A PROCESS FACILITY;” (ix) U.S. patentapplication Ser. No. 09\137,358 entitled “CONTROLLERS THAT DETERMINEOPTIMAL TUNING PARAMETERS FOR USE IN PROCESS CONTROL SYSTEMS AND METHODSOF OPERATING THE SAME;” and (x) U.S. patent application Ser. No.(Attorney Docket No. I20 25207), entitled “PROCESS FACILITY CONTROLSYSTEMS USING AN EFFICIENT PREDICTION FORM AND METHODS OF OPERATING THESAME” (which application is filed concurrently herewith), all of whichare commonly assigned to the assignee of the present invention. Thedisclosures of these related patents and patent applications areincorporated herein by reference for all purposes as if fully set forthherein.

COPYRIGHT NOTICE

[0002] A portion of the disclosure of this patent document (softwarelistings in APPENDICES A and B) contains material that is subject tocopyright protection. The copyright owner has no objection to thefacsimile reproduction by anyone of this patent document or the patentdisclosure, as it appears in the United States Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights and protection whatsoever.

TECHNICAL FIELD OF THE INVENTION

[0003] The present invention is directed, in general, to control systemsfor process facilities and, more specifically, to systems for generatingand using lookup tables with process facility control systems and modelsof the same, and methods of operating such systems, all for use tooptimize process facilities.

BACKGROUND OF THE INVENTION

[0004] Presently, process facilities (e.g., a manufacturing plant, amineral or crude oil refinery, etc.) are managed using distributedcontrol systems. Contemporary control systems include numerous modulestailored to control or monitor various associated processes of thefacility. Conventional means link these modules together to produce thedistributed nature of the control system. This affords increasedperformance and a capability to expand or reduce the control system tosatisfy changing facility needs.

[0005] Process facility management providers, such as HONEYWELL, INC.,develop control systems that can be tailored to satisfy wide ranges ofprocess requirements (e.g., global, local or otherwise) and facilitytypes (e.g., manufacturing, refining, etc.). A primary objective of suchproviders is to centralize control of as many processes as possible toimprove an overall efficiency of the facility. Each process, or group ofassociated processes, has certain input (e.g., flow, feed, power, etc.)and output (e.g., temperature, pressure, etc.) characteristicsassociated with it.

[0006] In recent years, model predictive control (“MPC”) techniques havebeen used to optimize certain processes as a function of suchcharacteristics. One technique uses algorithmic representations toestimate characteristic values (represented as parameters, variables,etc.) associated with them that can be used to better control suchprocesses. In recent years, physical, economic and other factors havebeen incorporated into control systems for these associated processes.Examples of such techniques are described in U.S. Pat. No. 5,351,184entitled “METHOD OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE,CONTROL;” U.S. Pat. No. 5,561,599 entitled “METHOD OF INCORPORATINGINDEPENDENT FEEDFORWARD CONTROL IN A MULTIVARIABLE PREDICTIVECONTROLLER;” U.S. Pat. No. 5,574,638 entitled “METHOD OF OPTIMAL SCALINGOF VARIABLES IN A MULTIVARIABLE PREDICTIVE CONTROLLER UTILIZING RANGECONTROL;” U.S. Pat. No. 5,572,420 entitled “METHOD OF OPTIMAL CONTROLLERDESIGN OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL” (the“'420 Patent”); U.S. patent application Ser. No. 08\850,288 entitled“SYSTEMS AND METHODS FOR GLOBALLY OPTIMIZING A PROCESS FACILITY;” U.S.patent application Ser. No. 08\851,590 entitled “SYSTEMS AND METHODSUSING BRIDGE MODELS TO GLOBALLY OPTIMIZE A PROCESS FACILITY;” and U.S.patent application Ser. No. 09\137,358 entitled “CONTROLLERS THATDETERMINE OPTIMAL TUNING PARAMETERS FOR USE IN PROCESS CONTROL SYSTEMSAND METHODS OF OPERATING THE SAME,” all of which are commonly owned bythe assignee of the present invention and incorporated herein above byreference for all purposes.

[0007] Generally speaking, one problem is that conventional efforts,when applied to specific processes, tend to be non-cooperative (e.g.,non-global, non-facility wide, etc.) and may, and all too often do,detrimentally impact the efficiency of the process facility as a whole.For instance, many MPC techniques control process variables topredetermined set points. Oftentimes the set points are a best estimateof a value of the set point or set points. When a process is beingcontrolled to a set point, the controller may not be able to achieve thebest control performances, especially under process/model mismatch.

[0008] To further enhance the overall performance of a control system,it is desirable to design a controller that deals explicitly with plantor model uncertainty. The '420 Patent, for example, teaches methods ofdesigning a controller utilizing range control. The controller isdesigned to control a “worst case” process. An optimal controller forthe process is achieved and, if the actual process is not a “worst caseprocess,” the performance of the controller is better than anticipated.

[0009] There are a number of well known PID “tuning” formulas, ortechniques, and the most common, or basic, PID algorithm includes threeknown user specified tuning parameters (κ, τ₁, τ₂) whose valuesdetermine how the controller will behave. These parameters aredetermined either by trial and error or through approaches that requireknowledge of the process. Although many of these approaches, which arecommonly algorithms, have provided improved control, PID controllerperformance tuned by such algorithms usually degrades as processconditions change, requiring a process engineer, or operator, to monitorcontroller performance. If controller performance deteriorates, theprocess engineer is required to “re-tune” the controller.

[0010] Controller performance deteriorates for many reasons, althoughthe most common cause is changing dynamics of the process. Since PIDcontroller performance has been related to the accuracy of the processmodel chosen, a need exists for PID controllers that allows for suchuncertainty by accounting for changing system dynamics. Further, therequirement for ever-higher performance control systems demands thatsystem hardware maximize software performance. Conventional controlsystem architectures are made up of three primary components: (i) aprocessor, (ii) a system memory and (iii) one or more input/outputdevices. The processor controls the system memory and the input/output(“I/O”) devices. The system memory stores not only data, but alsoinstructions that the processor is capable of retrieving and executingto cause the control system to perform one or more desired functions.The I/O devices are operative to interact with an operator through agraphical user interface, and with the facility as a whole through anetwork portal device and a process interface.

[0011] Over the years, the quest for ever-increasing process controlsystem speeds has followed different directions. One approach to improvecontrol system performance is to increase the rate of the clock thatdrives the system hardware. As the clock rate increases, however, thesystem hardware's power consumption and temperature also increase.Increased power consumption is expensive and high circuit temperaturesmay damage the process control system. Further, system hardware clockrate may not increase beyond a threshold physical speed at which signalsmay be processed. More simply stated, there is a practical maximum tothe clock rate that is acceptable to conventional system hardware.

[0012] An alternate approach to improve process control systemperformance is to increase the number of instructions executed per clockcycle by the system processor (“processor throughput”). One techniquefor increasing processor throughput calls for the processor to bedivided into separate processing stages. Instructions are processed inan “assembly line” fashion in the processing stages. Each processingstage is optimized to perform a particular processing function, therebycausing the processor as a whole to become faster. There is again apractical maximum to the clock rate that is acceptable to conventionalsystem hardware.

[0013] Since there are discernable physical limitations to whichconventional system hardware may be utilized, a need exists broadly foran approach that decreases the number of instructions required topreform the functions of the process control system. A need exists forsuch an approach that accounts for process uncertainty by accounting forchanging process dynamics.

SUMMARY OF THE INVENTION

[0014] To address the above-discussed deficiencies of the prior art, itis a primary object of the present invention to provide systems andmethods of operating such systems for populating and using lookup tableswith process facility control systems, as well as models of the same. Inaccordance with an exemplary embodiment below-discussed, the principlesof the present invention may be used to define and populate a lookuptable in response to the needs of a global controller. The lookup tableis populated with a range of possible values of at least one measurablecharacteristic associated with one or more processes of the processfacility and in accordance with a model of at least a portion of thesame.

[0015] Rather than calculate and re-calculate certain characteristicsassociated with a process or process model, which would consumesignificant system resources, the present invention introduces a datastructure capable of maintaining a range of possible values of one ormore of such certain characteristics. Use of the lookup table in lieu ofexecution and re-execution of the instructions for performingcharacteristic calculations decreases the number of instructionsrequired to preform the functions of the process control system. Thelookup table, once suitably populated, accounts for process uncertaintyby maintaining the range of possible values, thereby accounting forchanging process dynamics.

[0016] An exemplary computer system for use with a process facility thatis capable of populating a data structure in accordance with theprinciples of the present invention includes both a memory and aprocessor. The memory is capable of maintaining (i) the data structure,which has a plurality of accessible fields, and (ii) a model of at leasta portion of at least one process of a plurality of associated processesof the process facility. The model may advantageously include amathematical representation of at least a portion of the at least oneprocess, defining certain relationships among inputs and outputs of theat least one process. The processor is capable of populating ones of theplurality of accessible fields of the data structure using the modeliteratively with a range of possible values of the at least onemeasurable characteristic. The computer system is capable of using therange of possible values of the at least one measurable characteristicto predict an unforced response associated with the at least oneprocess.

[0017] In accordance with an important aspect hereof, the data structuremay be populated and maintained on-line (e.g., at a controller,distributed through a process control system, etc.), off-line (e.g.,standalone computer, computer network, etc.), or through some suitablecombination of the same. Likewise, the data structure may remain staticupon population, be dynamic, or be modifiable, at least in part.

[0018] Those skilled in the art will understand that “controllers” maybe implemented in hardware, software, or firmware, or some suitablecombination of the same, and, in general, that the use of computingsystems in control systems for process facilities is known. The phrase“associated with” and derivatives thereof, as used herein, may mean toinclude, be included within, interconnect with, contain, be containedwithin, connect to or with, couple to or with, be communicable with,cooperate with, interleave, be a property of, be bound to or with, have,have a property of, or the like; the term “include” and derivativesthereof, as used herein, are defined broadly, meaning inclusion withoutlimitation; and the term “or,” as used herein, means and/or.

[0019] The foregoing has outlined rather broadly the features andtechnical advantages of the present invention so that those skilled inthe art may better understand the detailed description of the inventionthat follows. Additional features and advantages of the invention willbe described hereinafter that form the subject of the claims of theinvention. Those skilled in the art should appreciate that they mayreadily use the conception and the specific embodiment disclosed as abasis for modifying or designing other structures for carrying out thesame purposes of the present invention. Those skilled in the art shouldalso realize that such equivalent constructions do not depart from thespirit and scope of the invention in its broadest form.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] For a more complete understanding of the present invention, andthe advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings,wherein like numbers designate like objects, and in which:

[0021]FIG. 1a illustrates a simple block diagram of an exemplary processfacility with which the present invention may be used;

[0022]FIG. 1b illustrates a detailed block diagram of one of theexemplary local controllers introduced in FIG. 1a;

[0023]FIG. 2 illustrates a flow diagram of an exemplary method forpopulating a data structure in accordance with the principles of thepresent invention; and

[0024]FIG. 3 illustrates an exemplary two-dimensional graphicalrepresentation of MV and PV curves in accordance with the principles ofthe present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0025] In accordance with the above-given summary, computer systems, andmethods of operating the same, are introduced herein for populating andusing lookup tables with process facility control systems, as well asmodels of the same. Before undertaking a detailed description of anadvantageous embodiment of the present invention, and discussing thevarious benefits and aspects of the same, it is useful to understandconceptually the operation and control structure of an exemplary processfacility.

[0026] Initial reference is therefore made to FIG. 1a, wherein a simpleblock diagram of such a process facility (generally designated 100) isillustrated. Exemplary process facility 100 is operative to process rawmaterials, and includes a control center 105, six associated processes110 a to 110 f that are arranged into three stages and a control system(generally designated 115). The term “include,” as well as derivativesthereof, as used throughout this patent document, is defined broadly tomean inclusion without limitation.

[0027] Exemplary control center 105 illustrates a central area that iscommonly operator manned (not shown) for centrally monitoring and forcentrally controlling the three exemplary process stages. A firstprocess stage includes three raw material grinders 110 a to 110 c thatoperate to receive a “feed” of raw material core and to grind the same,such as using a pulverizer or grinding wheel, into smaller particles ofraw material. The term “or,” as it is used throughout this patentdocument, is inclusive, meaning and/or. The second process stageincludes a washer 110 d that operates to receive the ground rawmaterials and clean the same to remove residue from the first stage. Thethird process stage includes a pair of separators 110 e and 110 f thatoperate to receive the ground and washed raw materials and separate thesame, such as into desired minerals and any remaining raw materials. Asthis process facility is provided for illustrative purposes only and theprinciples of such are known, further discussion of the same is beyondthe scope of this patent document.

[0028] Exemplary control system 115 illustratively includes a globalcontroller 120 and six local controllers 125 a to 125f, each of which isimplemented in software and executable by a suitable conventionalcomputer system (e.g., standalone, network, etc.), such as any ofHONEYWELL, INC.'s AM K2LCN, AM K4LCN, AM HMPU, AxM or like systems.Those skilled in the art will understand that such controllers may beimplemented in hardware, software, or firmware, or some suitablecombination of the same; in general, the use of computing systems incontrol systems for process facilities is known.

[0029] Global controller 120 is associated with each of localcontrollers 125, directly or indirectly, to allow communication ofinformation between the same. The phrase “associated with” andderivatives thereof, as used throughout this patent document, may meanto include within, interconnect with, contain, be contained within,connect to or with, couple to or with, be communicable with, cooperatewith, interleave, be a property of, be bound to or with, have, have aproperty of, or the like.

[0030] Global controller 120 monitors measurable characteristics (e.g.,status, temperature, utilization, efficiency, cost and other economicfactors, etc.) of associated processes 110, either directly orindirectly (as shown, through local controllers 125 associated withprocesses 110). Depending upon the implementation, such monitoring maybe of an individual process, group of processes, the facility as awhole, or otherwise. Similarly, local controllers 125 monitor associatedprocesses 110, and, more particularly, monitor certain characteristicsof associated processes 110.

[0031] Global controller 120 generates, in response to such monitoringefforts, control data that may be communicated via local controllers 125to associated processes 110 to optimize process

[0032] facility 100. The phrase “control data,” as used herein, isdefined as any numeric, qualitative or other value generated by globalcontroller 120 to globally control (e.g., direct, manage, modify,recommend to, regulate, suggest to, supervise, cooperate, etc.) aparticular process, a group of processes, a facility, a process stage, agroup of process stages, a sequence of processes or process stages, orthe like to optimize the facility. Local controllers 125 operate tovarying degrees in accordance with the control data to control theassociated processes, and, more particularly, to modify one or moreprocesses and improve the monitored characteristics and the facility.

[0033] According to an advantageous embodiment, the control data may bedynamically generated using a lookup table defined and populated inaccordance with the principles hereof, and such control data generationis based, at least in part, upon a given facility's efficiency,production or economic cost, and, most preferably, all three. The lookuptable may be populated and maintained on-line (e.g., at globalcontroller 120, at local controller 125, distributed within controlsystem 115, etc.), off-line (e.g., standalone computer, networkcomputer, etc.), or through some suitable combination of the same;likewise, the lookup table may be static upon population, be dynamic, orbe modifiable, at least in part.

[0034] The global controller 120 and the local controllers 125 maysuitably use one or more such lookup tables to control processes 110 toconserve processing resources and increase the overall speed of controlsystem 115. Control system 115 achieves a high level of both global andlocal monitoring, and cooperative control of associated processes 110among controllers 120 and 125, by allowing the local controllers 125 tovary their individual or respective compliance with the control data.Varying degrees of compliance by local controllers 125 may range betweenfull compliance and noncompliance. The relationship between globalcontroller 120 and various ones of local controllers 110 may bemaster-slave (full compliance), cooperative (varying compliance, e.g.,using control data as a factor in controlling the associated processes),complete disregard (noncompliance), as well as anywhere along thatrange.

[0035] Depending upon the implementation and needs of a given facility,the relationship between global controller 120 and specific localcontrollers 125 may be static ( i.e., always only one of compliance,cooperative, or noncompliance), dynamic (i.e., varying over time, suchas within a range between compliance and noncompliance, some lesserrange therebetween, or otherwise), or varying between the same. One ormore specific processes 110, and facility 100 as a whole, may bedynamically and cooperatively controlled as a function of local andglobal optimization efforts, and such dynamic and cooperative control isindependent of the relationship between global controller 120 andspecific local 125, as described above.

[0036] Turning to FIG. 1b, illustrated is a more detailed block diagramof one of the exemplary local controllers 125 that is associated withone or group of associated processes 110. Local controller 125 uses asingle loop model predictive control (“SL-MPC”) structure that uses anefficient matrix prediction form in accordance with the principles ofthe present invention, as well as an analytical control solution map toreduce utilization of processing resources relative to conventional MPCtechnology.

[0037] According to the illustrated embodiment, local controller 125receives as inputs, control/optimization specifications 130 (e.g.,bounds, ranges, tolerances, control points, etc.) and feedback data 135(e.g., output of associated process 110). Control/optimizationspecifications 130 may be received from any of a number of sourcesdepending upon the associated process or group of associated processes110, an associated process facility or any other factor. For example,any of control/optimization specifications 130 may be received from anoperator of a control center for the associated process facility,retrieved from a database or data repository, received from anotherassociated controller (e.g., one or more local controllers 125, globalcontroller 120, or a suitable combination thereof), etc.

[0038] Control/optimization specifications 130 include two types ofvariables: (1) a first variable (“MV”) that may be manipulated, such asflow, feed, air blower, etc; and (2) a second variable (“V”) that cannotbe manipulated and is a disturbance variable, such as burn rate, fuelquality per unit, etc. Feedback data 135 is a third variable (“CV”) thatis responsive to MVs and DVs, and is an output of associated process110, such as pressure, temperature, etc. A sub-variable (“PV”) ofFeedback data 135 is indicative of the iterative response of theassociated process 110 to monitoring and control by the local controller125. Many, if not all, of such MVs, DVs and CVs represent measurablecharacteristics of associated process 110 that may be suitably monitoredby local controller 125.

[0039] Local controller 125 includes a dynamic prediction task withstate estimation 150, a local linear program/quadratic program (“LP/QP”)optimization task 155, a dynamic control/optimization augmented rangecontrol algorithm (“RCA”) 160 and a lookup table 165. Exemplary dynamicprediction task 150 receives CVs and operates to generate an array ofmultiple predictions (or dynamic unforced predictions) and, at 5 tau(response time close to end), an unforced prediction for valuesassociated with associated process 110. The CVs represent feedback data135 (e.g., inputs, outputs, etc.) associated with process 105, anddynamic prediction task 150 operates to accesses lookup table 165 andselects one or more values from the range of possible values, suchselection being responsive, at least in part, to the received feedbackdata 135. A preferred method of using data structures, such as lookuptable 165, or functionally equivalent dedicated circuitry, to maintain arange of possible values for one or more measurable characteristicsassociated with a process is disclosed and described in U.S. patentapplication Ser. No. (Attorney Docket No. I20 25207), entitled “PROCESSFACILITY CONTROL SYSTEMS USING AN EFFICIENT PREDICTION FORM AND METHODSOF OPERATING THE SAME” and filed concurrently herewith, the disclosureof which has previously been incorporated herein by reference for allpurposes as if fully set forth herein.

[0040] Exemplary local LP/QP optimization task 155 receives optimizationspecifications 140 a and, in response to the unforced prediction,operates to generate, at 5 tau, optimal values associated withassociated process 110.

[0041] A preferred method of performing the foregoing task is disclosedand described in U.S. Pat. No. 5,758,047, entitled “METHOD OF PROCESSCONTROLLER OPTIMIZATION IN A MULTIVARIABLE PREDICTIVE CONTROLLER,” whichis commonly owned by the assignee of this patent document and relatedinvention, the disclosure of which has previously been incorporatedherein by reference for all purposes as if fully set forth herein. Mostpreferably, optimization specifications 140 a are associated, directlyor indirectly, with an economic value of the output of associatedprocess 110. According to an advantageous embodiment, the unforcedprediction may suitably be represented as a single variable and theLP/QP optimization task may be a linear determination of a minimum valueor a maximum value, or a quadratic determination of a desired value.Exemplary dynamic control/optimization augmented RCA 160 receivescontrol specifications 140 b and, in response to receiving the array ofmultiple predictions (from dynamic prediction task 150) and the optimalvalues (from local LP/QP optimization task 155), operates to generatecontrol values, the MVs, that are input to associated process 110. Animportant aspect of exemplary local controller 125 is the use ofcontrol/optimization specifications 140 and feedback data 135 to locallyunify economic/operational optimization with MPC dynamically for aspecific process or group of processes.

[0042] Note the distinction between the foregoing discussion whichintroduces a very powerful multi-loop MPC embodiment having a welldefined and dynamic interaction/interleaving relation among global andlocal controllers and the single loop controller embodiment described inU.S. patent application Ser. No. (Attorney Docket No. I20 25207), thedisclosure of which has previously been incorporated herein by referencefor all purposes. Those skilled in the art will understand therelationship among these embodiments and the applicability of theprinciples of the present invention.

[0043] Turning now to FIG. 2, illustrated is a flow diagram of anexemplary method (generally designated 200) for populating a datastructure 165, shown as a lookup table, in accordance with theprinciples of the present invention (this discussion of FIG. 2 makesconcurrent reference to FIGS. 1a and 1 b). The phrase “data structure,”as the same is used herein, is defined broadly as any syntacticstructure of expressions, data or other values or indicia, includingboth logical and physical structures. A data structure may therefore beany array (i.e., any arrangement of objects into one or more dimensions,e.g., a matrix, a table, etc.), or other like grouping, organization, orcategorization of objects in accordance herewith.

[0044] For purposes of illustration, a processor 205 and a memory 210are introduced. Exemplary memory 210 is operative to store, or tomaintain, lookup table 165, along with the various tasks/instructions(generally designated 215) comprising method 200. Exemplary processor205 is operative to select and execute tasks/instructions 215 which, inturn, cause processor 205 to perform the functions of method 200.

[0045] To begin, processor 205 is directed through the execution ofmethod 200 (e.g., manually (i.e., through interaction with an operator),automatically, or partially-automatically) to define a model 220 of atleast a portion of at least one of the associated processes 110 (processstep 225). Processor 205 is directed to store model 220 in memory 210,preferably representing at least a portion of process 110mathematically. The mathematical representation defines one or morerelationships among inputs and outputs of process 110.

[0046] According to an advantageous embodiment, model 220 is definedusing the following discrete state space model form:

x _(k+1) =Ax _(k) +Bu _(k)  (1)

y _(k) =Cx _(k) +Du _(k)  (2)

[0047] wherein X_(k), U_(k), and y_(k) represent various states ofmodeled process 110, wherein k is a time period and k+1 is a next timeperiod, and A, B, C, and D respectively represent measurablecharacteristics of modeled process 110 at any given time period.

[0048] Processor 205 is directed to define a data structure, such aslookup table 165, having a plurality of accessible fields (process step230). An exemplary source code embodiment for performing this definitionis attached as APPENDIX A, and incorporated herein by reference as iffully set forth herein, and that is written in Pascal. Depending uponthe needs of the particular implementation, the contents of suchaccessible fields may suitably be nulled, defaulted, or otherwiseinitialized or used. Memory 210, directed by processor 205, maintainslookup table 165, preferably representing, at least in part, an AB0Imatrix 235 and a feedback vector 240.

[0049] According to an advantageous embodiment, AB0I matrix 235 andfeedback vector 240 have the following respective definitions:$\begin{matrix}\begin{bmatrix}A & B \\0 & I\end{bmatrix} & (3) \\\begin{bmatrix}x_{k} \\u_{k}\end{bmatrix} & (4)\end{matrix}$

[0050] wherein I and 0 respectively and illustratively represent anidentity matrix and an null matrix, for the purpose of this illustrativemodel, to maintain, or hold, MV constant (illustrated with respect toFIG. 3).

[0051] Processor 205 is directed to delineate mathematically arelationship among the above-given matrix 235 and vector 240 (processstep 245), which according to an advantageous embodiment, has thefollowing form: $\begin{matrix}{{z_{k + 1} = {\begin{bmatrix}A & B \\0 & I\end{bmatrix}Z_{k}}},\quad {z_{k} = \begin{bmatrix}x_{k} \\u_{k}\end{bmatrix}}} & (5)\end{matrix}$

[0052] Processor 205 is directed to delineate mathematically arelationship among the above-given discrete state space model form andthe Z vector 240 (process step 250), which, according to an advantageousembodiment, gives the following prediction form for any p interval, orpoint in the future: $\begin{matrix}{{{\hat{y}\left( {k + p} \right)}k} = {{\lbrack{CD}\rbrack \begin{bmatrix}A & B \\0 & I\end{bmatrix}}^{p}Z_{k}}} & (6)\end{matrix}$

[0053] Stated generally, use of Z vector 240 represents, or defines,mathematically, the relationship among the one or more inputs andoutputs of modeled process 110.

[0054] For a variety of purposes, as above-stated, for monitoring andfor control of process 110, it is desirable to decrease utilization ofprocessing resources. This may be accomplished, in part, through arecognition that certain characteristics of process 110 are measurable(e.g., appraising, assessing, gauging, valuating, estimating, comparing,computing, rating, grading, synchronizing, analyzing, etc.), whether ornot such characteristics are dependent, independent, interdependent, orotherwise effected by other characteristics of the same process, a groupof processes, a facility, a process stage, a group of process stages, asequence of processes or process stages, or the like. Many of thesemeasurable characteristics have a range of possible values, which may ormay not change, or vary, over time. It is desirable, in the presentexample, to determine an efficient prediction form (“EPF”), the range ofvalues of which may suitably be maintained in lookup table 165.

[0055] Processor 205 is directed to populate ones of the accessiblefields 255 of lookup table 165 with a range of possible values of atleast one measurable characteristic associated with at least process 110(process step 260). An exemplary source code embodiment for performingthis population is attached as APPENDIX B, and incorporated herein byreference as if fully set forth herein, and that is written in Pascal.According to the illustrative embodiment, it is desirable to have futurepredictions available, or precalculated, which may suitably be stored asan array of points within lookup table 165. This collection of pointsmay be referred to as PV-blocking, which may be given by the followingform for any p_(i) interval, or point in the future: $\begin{matrix}{{{\hat{Y}\left( {k + {pv} - {blocking}} \right)}k} = \begin{bmatrix}{{\hat{y}\left( {k + p_{1}} \right)}k} \\{{\hat{y}\left( {k + p_{2}} \right)}k} \\\vdots \\{{\hat{y}\left( {k + p_{m}} \right)}k}\end{bmatrix}} & (7)\end{matrix}$

[0056] wherein i is the index for PV-blocking. The foregoing calculationmay suitably be condensed into a product of EPF and Z_(k), which may begiven by:

{circumflex over (y)}(k+pv-blocking)¦k=[EPF]z _(k)  (8)

[0057] wherein EPF may be given by: $\begin{matrix}{\lbrack{EPF}\rbrack = \begin{bmatrix}{epf}_{1} \\{epf}_{2} \\\vdots \\{epf}_{m}\end{bmatrix}} & (9)\end{matrix}$

[0058] wherein epf_(i) is independent from the feed back informationcontained in the Z vector and may therefore be calculated in advance andgiven by: $\begin{matrix}{{epf}_{i} = {\lbrack{CD}\rbrack \begin{bmatrix}A & B \\0 & I\end{bmatrix}}^{p_{i}}} & (10)\end{matrix}$

[0059] In short, exemplary processor 205 uses model 220 iteratively, orincrementally, to populate lookup table 165 with k possible values,thereby defining a range of values. A v_(k) vector is formulated toconveniently calculate both Z_(k) and Y_((k+pvblocking)¦k) for differentincremental k, which has the following form: $\begin{matrix}{{V_{k + 1} = {\begin{bmatrix}{AB} \\{0I} \\{EPF}\end{bmatrix}Z_{k}}},\quad {V_{k} = \begin{bmatrix}z_{k} \\{{\hat{y}\left( {k + {pvblocking}} \right)}k}\end{bmatrix}}} & (11)\end{matrix}$

[0060] Turning momentarily to FIG. 3, illustrated is an exemplarytwo-dimensional graphical representation of MV and PV curves inaccordance with a use of lookup table 165 in accordance with the controlsystem 100 of FIGS. 1a and 1 b and the principles of the presentinvention. It should be noted, that FIGS. 1a, 1 b, 2, and 3, along withthe various embodiments used to describe the principles of the presentinvention in this patent document are illustrative only. To that end,alternate embodiments of model 220 may define any particular process, agroup of processes, a facility, a process stage, a group of processstages, an interrelationship among, or a sequence of, processes orprocess stages, or some suitable portion or combination of any of thesame. It should be further noted that a matrix structure was chosen forthe EPF in this embodiment, however, alternate embodiments may use anyappropriate data structure or dedicated circuitry to create a suitablyarranged lookup array, or table, or the like. Such data structures anddedicated circuitry may be populated off-line, on-line or through somesuitable combination of the same; likewise, such populated datastructures and dedicated circuitry may be static, dynamic, modifiable,centralized, distributed, or any suitable combination of the same.

[0061] Those of ordinary skill in the art should recognize that thecomputer system 105 described using processor 205 and memory 210 may beany suitably arranged hand-held, laptop/notebook, mini, mainframe orsuper computer, as well as network combination of the same. In point offact, alternate embodiments of computer system 205 may include, or bereplaced by, or combined with, any suitable circuitry, includingprogrammable logic devices, such as programmable array logic (“PALs”)and programmable logic arrays (“PLAs”), digital signal processors(“DSPs”), field programmable gate arrays (“FPGAs”), application specificintegrated circuits (“ASICs”), very large scale integrated circuits(“VLSIs”) or the like, to form the processing systems described andclaimed herein. To that end, while the disclosed embodiments requireprocessor 205 to access and to execute a stored task/instructions frommemory to perform the various functions described hereabove, alternateembodiments may certainly be implemented entirely or partially inhardware. Conventional processing system architecture is more fullydiscussed in Computer Organization and Architecture, by WilliamStallings, MacMillan Publishing Co. (3rd ed. 1993); conventionalprocessing system network design is more fully discussed in Data NetworkDesign, by Darren L. Spohn, McGraw-Hill, Inc. (1993); and conventionaldata communications is more fully discussed in Data CommunicationsPrinciples, by R. D. Gitlin, J. F. Hayes and S. B. Weinstein, PlenumPress (1992) and in The Irwin Handbook of Telecommunications, by JamesHarry Green, Irwin Professional Publishing (2nd ed. 1992). Each of theforegoing publications is incorporated herein by reference for allpurposes.

What is claimed is:
 1. A computer system for use with a process facilityhaving a plurality of associated processes, comprising: circuitry thatis capable of maintaining a data structure having a plurality ofaccessible fields; and a processor, associated with said circuitry, thatis capable of populating ones of said plurality of accessible fields ofsaid data structure with a range of possible values of at least onemeasureable characteristic associated with at least one process of saidplurality of associated processes.
 2. The computer system set forth inclaim 1 wherein said circuitry is capable of storing a task that directssaid processor to populate said ones of said plurality of accessiblefields of said data structure with said range of possible values.
 3. Thecomputer system set forth in claim 1 wherein said circuitry is furthercapable of maintaining a model of at least a portion of said pluralityof associated processes.
 4. The computer system set forth in claim 3wherein said model includes a mathematical representation of at least aportion of said at least one process of said plurality of associatedprocesses, said mathematical representation defining relationships amonginputs and outputs of said at least one process of said associatedprocesses.
 5. The computer system set forth in claim 3 wherein saidprocessor is capable of using said model iteratively to populate ones ofsaid plurality of accessible fields of said data structure with saidrange of possible values of said at least one measureablecharacteristic.
 6. The computer system set forth in claim 5 wherein saidmodel includes at least one feedback variable representing, at least inpart, an output of said at least one process of said associatedprocesses.
 7. The computer system set forth in claim 6 wherein saidprocessor populates at least one of said plurality of accessible fieldsof said data structure in response to said at least one feedbackvariable of said at least one process of said associated processes. 8.The computer system set forth in claim 3 wherein said model includes amanipulable variable.
 9. The computer system set forth in claim 8wherein said processor is capable of at least substantially maintaininga value of said manipulable variable during at least a portion of saiditerative population of said ones of said plurality of accessible fieldsof said data structure.
 10. The computer system set forth in claim 1,wherein said circuitry maintains statically said range of possiblevalues of said at least one measureable characteristic associated withat least one process of said plurality of associated processes.
 11. Thecomputer system set forth in claim 1 wherein said processor is furthercapable of using said range of possible values of said at least onemeasureable characteristic to predict an unforced response associatedwith said at least one process.
 12. A method of operating a computersystem that is for use with a process facility having a plurality ofassociated processes, said method of operation comprising the steps of:maintaining a data structure having a plurality of accessible fields incircuitry associated with said computer system; and populating ones ofsaid plurality of accessible fields of said data structure using aprocessor, that is associated with said circuitry, with a range ofpossible values of at least one measurable characteristic associatedwith at least one process of said plurality of associated processes. 13.The method of operation set forth in claim 12 further comprising thestep of storing a task in said circuitry that is capable of directingsaid processor to populate said ones of said plurality of accessiblefields of said data structure with said range of possible values. 14.The method of operation set forth in claim 12 further comprising thestep of maintaining a model of at least a portion of said plurality ofassociated processes in said circuitry.
 15. The method of operation setforth in claim 14 wherein said model includes a mathematicalrepresentation of at least a portion of said at least one process ofsaid plurality of associated processes, said mathematical representationdefining relationships among inputs and outputs of said at least oneprocess of said associated processes, and said method further comprisesthe step of using said model iteratively by said processor to populateones of said plurality of accessible fields of said data structure withsaid range of possible values of said at least one measurablecharacteristic.
 16. The method of operation set forth in claim 15wherein said model includes at least one feedback variable representing,at least in part, an output of said at least one process of saidassociated processes, and said method further comprises the step ofusing said processor, in response to said at least one feedback variableof said at least one process of said associated processes, to populateat least one of said plurality of accessible fields of said datastructure.
 17. The method of operation set forth in claim 14 whereinsaid model includes a manipulable variable, and said method furthercomprises the step of at least substantially maintaining a value of saidmanipulable variable during at least a portion of said iterativepopulation of said ones of said plurality of accessible fields of saiddata structure.
 18. The method of operation set forth in claim 12wherein said circuitry maintains statically said range of possiblevalues of said at least one measurable characteristic associated with atleast one process of said plurality of associated processes.
 19. Themethod of operation set forth in claim 12 further comprising the step ofpredicting an unforced response associated with said at least oneprocess using said processor and said range of possible values of saidat least one measurable characteristic.
 20. A data structure for usewith a computer system associated with a process facility having aplurality of associated processes, said data structure comprising aplurality of accessible fields, ones of said plurality of accessiblefields maintaining a range of possible values of at least one measurablecharacteristic associated with at least one process of said plurality ofassociated processes.
 21. The data structure set forth in claim 20wherein one of said plurality of accessible fields are capable of beingselected by said computer system.